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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/managed/VertexAIDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Google Cloud LlamaIndex on Vertex AI for RAG\n",
    "\n",
    "In this notebook, we will show you how to get started with the [Vertex AI RAG API](https://cloud.google.com/vertex-ai/generative-ai/docs/llamaindex-on-vertexai).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install llama-index-llms-gemini\n",
    "%pip install llama-index-indices-managed-vertexai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install llama-index\n",
    "%pip install google-cloud-aiplatform==1.53.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup\n",
    "\n",
    "Follow the steps in this documentation to create a Google Cloud project and enable the Vertex AI API.\n",
    "\n",
    "https://cloud.google.com/vertex-ai/docs/start/cloud-environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Authenticating your notebook environment\n",
    "\n",
    "* If you are using **Colab** to run this notebook, run the cell below and continue.\n",
    "* If you are using **Vertex AI Workbench**, check out the setup instructions [here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "# Additional authentication is required for Google Colab\n",
    "if \"google.colab\" in sys.modules:\n",
    "    # Authenticate user to Google Cloud\n",
    "    from google.colab import auth\n",
    "\n",
    "    auth.authenticate_user()\n",
    "\n",
    "    ! gcloud config set project {PROJECT_ID}\n",
    "    ! gcloud auth application-default login -q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir -p 'data/paul_graham/'\n",
    "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Usage\n",
    "\n",
    "A `corpus` is a collection of `document`s. A `document` is a body of text that is broken into `chunk`s."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set up LLM for RAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import Settings\n",
    "from llama_index.llms.vertex import Vertex\n",
    "\n",
    "vertex_gemini = Vertex(\n",
    "    model=\"gemini-1.5-pro-preview-0514\",\n",
    "    temperature=0,\n",
    "    context_window=100000,\n",
    "    additional_kwargs={},\n",
    ")\n",
    "\n",
    "Settings.llm = vertex_gemini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.indices.managed.vertexai import VertexAIIndex\n",
    "\n",
    "# TODO(developer): Replace these values with your project information\n",
    "project_id = \"YOUR_PROJECT_ID\"\n",
    "location = \"us-central1\"\n",
    "\n",
    "# Optional: If creating a new corpus\n",
    "corpus_display_name = \"my-corpus\"\n",
    "corpus_description = \"Vertex AI Corpus for LlamaIndex\"\n",
    "\n",
    "# Create a corpus or provide an existing corpus ID\n",
    "index = VertexAIIndex(\n",
    "    project_id,\n",
    "    location,\n",
    "    corpus_display_name=corpus_display_name,\n",
    "    corpus_description=corpus_description,\n",
    ")\n",
    "print(f\"Newly created corpus name is {index.corpus_name}.\")\n",
    "\n",
    "# Upload local file\n",
    "file_name = index.insert_file(\n",
    "    file_path=\"data/paul_graham/paul_graham_essay.txt\",\n",
    "    metadata={\n",
    "        \"display_name\": \"paul_graham_essay\",\n",
    "        \"description\": \"Paul Graham essay\",\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that what we've ingested."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(index.list_files())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's ask the index a question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Querying.\n",
    "query_engine = index.as_query_engine()\n",
    "response = query_engine.query(\"What did Paul Graham do growing up?\")\n",
    "\n",
    "# Show response.\n",
    "print(f\"Response is {response.response}\")\n",
    "\n",
    "# Show cited passages that were used to construct the response.\n",
    "for cited_text in [node.text for node in response.source_nodes]:\n",
    "    print(f\"Cited text: {cited_text}\")\n",
    "\n",
    "# Show answerability. 0 means not answerable from the passages.\n",
    "# 1 means the model is certain the answer can be provided from the passages.\n",
    "if response.metadata:\n",
    "    print(\n",
    "        f\"Answerability: {response.metadata.get('answerable_probability', 0)}\"\n",
    "    )"
   ]
  }
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